Abstract

Vehicular Ad hoc Networks (VANETs) have emerged as a promising technology for enabling communication among vehicles and infrastructure. However, the dynamic nature of VANETs poses significant challenges, including interference and channel congestion, which severely impact the reliability and efficiency of communication. This research proposes a novel approach named Interference and Congestion Mitigation in VANET using LSTM-Backpropagation (ICMV-LB) to address these challenges by leveraging Long Short-Term Memory (LSTM) networks trained with Backpropagation. The proposed method aims to predict and mitigate interference and channel congestion in VANETs by exploiting the temporal dependencies present in the network data. LSTM networks, a type of recurrent neural network (RNN) known for their ability to capture long-term dependencies, are employed to learn the patterns and dynamics of interference and congestion in VANETs. By training the LSTM networks with Backpropagation, the models are optimized to accurately predict future interference and congestion levels based on historical data. Furthermore, the predicted interference and congestion levels are utilized to dynamically adjust communication parameters, such as transmission power and frequency allocation, in real-time to mitigate the adverse effects on communication performance. This adaptive approach enables VANETs to maintain reliable and efficient communication even in highly dynamic and congested environments. To evaluate the effectiveness of the proposed approach, extensive simulations are conducted using realistic VANET scenarios using SUMO – Simulator for Urban Environment. Results demonstrate significant improvements in communication reliability, throughput, latency, etc. compared to traditional approaches. This research highlights the potential of ICMV-LB, thereby enhancing the overall performance and reliability of vehicular communication systems.